Attributes Guided Feature Learning for Vehicle Re-Identification
文献类型：期刊论文
| 作者 | Li, Hongchao4; Lin, Xianmin4; Zheng, Aihua3; Li, Chenglong3; Luo, Bin4; He, Ran2 ; Hussain, Amir1
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| 刊名 | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
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| 出版日期 | 2021-11-30 |
| 页码 | 11 |
| 关键词 | Feature extraction Cameras Color Image color analysis Task analysis Training Semantics Attributes deep features vehicle re-identification |
| ISSN号 | 2471-285X |
| DOI | 10.1109/TETCI.2021.3127906 |
| 通讯作者 | Luo, Bin(ahu_lb@163.com) |
| 英文摘要 | Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance. However, it suffers from large intra-class variation caused by view variations and illumination changes, and inter-class similarity especially for different identities with a similar appearance. To handle these issues, in this paper, we propose a novel deep network architecture, which guided by meaningful attributes including camera views, vehicle types and colors for vehicle Re-ID. In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes. For network training, we annotate the view labels on the VeRi-776 dataset. Note that one can directly adopt the pre-trained view (as well as type and color) subnetwork on the other datasets with only ID information, which demonstrates the generalization of our model. Extensive experiments on the benchmark datasets VeRi-776 and VehicleID suggest that the proposed approach achieves the promising performance and yields to a new state-of-the-art for vehicle Re-ID. |
| WOS关键词 | PERSON REIDENTIFICATION ; NETWORK |
| 资助项目 | National Key Research and Development Program of China[2018AAA0100400] ; National Natural Science Foundation of China[61976002] ; National Natural Science Foundation of China[61976003] ; National Natural Science Foundation of China[62076003] ; National Natural Science Foundation of China[61860206004] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000727918400001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46797]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Luo, Bin |
| 作者单位 | 1.Edinburgh Napier Univ, Sch Comp, Edinburgh EH10 5DT, Midlothian, Scotland 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Anhui Univ, Sch Artificial Intelligence, Anhui Prov Key Lab Multimodal Cognit Computat, Informat Mat & Intelligent Sensing Lab Anhui Prov, Hefei 230601, Peoples R China 4.Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Hongchao,Lin, Xianmin,Zheng, Aihua,et al. Attributes Guided Feature Learning for Vehicle Re-Identification[J]. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,2021:11. |
| APA | Li, Hongchao.,Lin, Xianmin.,Zheng, Aihua.,Li, Chenglong.,Luo, Bin.,...&Hussain, Amir.(2021).Attributes Guided Feature Learning for Vehicle Re-Identification.IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,11. |
| MLA | Li, Hongchao,et al."Attributes Guided Feature Learning for Vehicle Re-Identification".IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2021):11. |
入库方式： OAI收割
来源：自动化研究所
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